Different from traditional segmentation which only focuses on visible regions, amodal instance segmentation can predict the occluded parts of object instances. Li and Malik [1]} first propose a method by extending [2]}, which iteratively enlarges the modal bounding box following the direction of high heatmap values and synthetically adds occlusion. Zhu et al. [3]} propose a COCO amodal dataset with 5000 images from the original COCO and use AmodalMask as a baseline, which is SharpMask [4]} trained on amodal ground truth. COCOA cls [5]} augments this dataset by assigning class-labels to the objects while SAIL-VOS dataset in [6]} is targeted for video object segmentation. In autonomous driving, Qi et al. [7]} establish the large-scale KITTI [8]} InStance segmentation dataset (KINS) and present ASN to improve amodal segmentation performance.
